





'''simple docstring'''






from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase									=   logging.get_logger(__name__)
class 		lowerCamelCase	(   _A							):
	snake_case_   = ["pixel_values"]



	def __init__( self		,   a_ = True		,   a_ = None		,   a_ = PILImageResampling.BICUBIC		,   a_ = True		,   a_ = None		,   a_ = True		,   a_ = 1 / 255		,   a_ = True		,   a_ = IMAGENET_DEFAULT_MEAN		,   a_ = IMAGENET_DEFAULT_STD		,   **a_		,   ):
			super().__init__(**a_						)
			lowerCAmelCase  :     Optional[int]       						= size if size is not None else {"shortest_edge": 224}
			lowerCAmelCase  :     int       						= get_size_dict(a_		,   default_to_square=a_						)
			lowerCAmelCase  :     List[Any]       						= crop_size if crop_size is not None else {"height": 224, "width": 224}
			lowerCAmelCase  :     Union[str, Any]       						= get_size_dict(a_		,   param_name="crop_size"						)

			lowerCAmelCase  :     Dict       						= do_resize
			lowerCAmelCase  :     Union[str, Any]       						= size
			lowerCAmelCase  :     List[Any]       						= resample
			lowerCAmelCase  :     List[Any]       						= do_center_crop
			lowerCAmelCase  :     Union[str, Any]       						= crop_size
			lowerCAmelCase  :     Union[str, Any]       						= do_rescale
			lowerCAmelCase  :     Optional[int]       						= rescale_factor
			lowerCAmelCase  :     Optional[int]       						= do_normalize
			lowerCAmelCase  :     Any       						= image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
			lowerCAmelCase  :     int       						= image_std if image_std is not None else IMAGENET_DEFAULT_STD



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_ = PILImageResampling.BICUBIC		,   a_ = None		,   **a_		,   ):
			lowerCAmelCase  :     str       						= get_size_dict(a_		,   default_to_square=a_						)
			# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
			if "shortest_edge" in size:
					lowerCAmelCase  :     List[Any]       						= int((256 / 224) * size["shortest_edge"]						)
					lowerCAmelCase  :     Optional[Any]       						= get_resize_output_image_size(a_		,   size=a_		,   default_to_square=a_						)
					lowerCAmelCase  :     Optional[int]       						= {"height": output_size[0], "width": output_size[1]}
			if "height" not in size_dict or "width" not in size_dict:
					raise ValueError(
					    F'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'''						)
			return resize(
			    a_		,   size=(size_dict["height"], size_dict["width"])		,   resample=a_		,   data_format=a_		,   **a_						)



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_ = None		,   **a_		,   ):
			lowerCAmelCase  :     int       						= get_size_dict(a_						)
			if "height" not in size or "width" not in size:
					raise ValueError(F'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'''						)
			return center_crop(a_		,   size=(size["height"], size["width"])		,   data_format=a_		,   **a_						)



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_ = None		,   **a_		,   ):
			return rescale(a_		,   scale=a_		,   data_format=a_		,   **a_						)



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_		,   a_ = None		,   **a_		,   ):
			return normalize(a_		,   mean=a_		,   std=a_		,   data_format=a_		,   **a_						)





	def 	_lowerCamelCase     ( self		,   a_		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = ChannelDimension.FIRST		,   **a_		,   ):
			lowerCAmelCase  :     Optional[Any]       						= do_resize if do_resize is not None else self.do_resize
			lowerCAmelCase  :     Any       						= resample if resample is not None else self.resample
			lowerCAmelCase  :     Optional[Any]       						= do_center_crop if do_center_crop is not None else self.do_center_crop
			lowerCAmelCase  :     Optional[int]       						= do_rescale if do_rescale is not None else self.do_rescale
			lowerCAmelCase  :     str       						= rescale_factor if rescale_factor is not None else self.rescale_factor
			lowerCAmelCase  :     Optional[int]       						= do_normalize if do_normalize is not None else self.do_normalize
			lowerCAmelCase  :     Optional[int]       						= image_mean if image_mean is not None else self.image_mean
			lowerCAmelCase  :     Tuple       						= image_std if image_std is not None else self.image_std

			lowerCAmelCase  :     str       						= size if size is not None else self.size
			lowerCAmelCase  :     Tuple       						= get_size_dict(a_		,   default_to_square=a_						)
			lowerCAmelCase  :     List[str]       						= crop_size if crop_size is not None else self.crop_size
			lowerCAmelCase  :     List[Any]       						= get_size_dict(a_		,   param_name="crop_size"						)

			lowerCAmelCase  :     Tuple       						= make_list_of_images(a_						)

			if not valid_images(a_						):
					raise ValueError(
					    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
					    "torch.Tensor, tf.Tensor or jax.ndarray."						)

			if do_resize and size is None:
					raise ValueError("Size must be specified if do_resize is True."						)

			if do_center_crop and crop_size is None:
					raise ValueError("Crop size must be specified if do_center_crop is True."						)

			if do_rescale and rescale_factor is None:
					raise ValueError("Rescale factor must be specified if do_rescale is True."						)

			if do_normalize and (image_mean is None or image_std is None):
					raise ValueError("Image mean and std must be specified if do_normalize is True."						)

			# All transformations expect numpy arrays.
			lowerCAmelCase  :     Optional[int]       						= [to_numpy_array(a_						) for image in images]

			if do_resize:
					lowerCAmelCase  :     Union[str, Any]       						= [self.resize(a_		,   a_		,   a_						) for image in images]

			if do_center_crop:
					lowerCAmelCase  :     int       						= [self.center_crop(a_		,   a_						) for image in images]

			if do_rescale:
					lowerCAmelCase  :     List[Any]       						= [self.rescale(a_		,   a_						) for image in images]

			if do_normalize:
					lowerCAmelCase  :     List[str]       						= [self.normalize(a_		,   a_		,   a_						) for image in images]

			lowerCAmelCase  :     Any       						= [to_channel_dimension_format(a_		,   a_						) for image in images]

			lowerCAmelCase  :     Optional[int]       						= {"pixel_values": images}
			return BatchFeature(data=a_		,   tensor_type=a_						)










'''simple docstring'''






def   __A    (				a_    :	list[list[float]]						):
		lowerCAmelCase  :     list[list[float]]       						= []
		for data in source_data:
				for i, el in enumerate(a_						):
						if len(a_						) < i + 1:
								data_lists.append([]						)
						data_lists[i].append(float(a_						)						)
		return data_lists






def   __A    (				a_    :	list[list[float]]   ,a_    :	list[int]						):
		lowerCAmelCase  :     list[list[float]]       						= []
		for dlist, weight in zip(a_   ,a_						):
				lowerCAmelCase  :     Optional[Any]       						= min(a_						)
				lowerCAmelCase  :     List[str]       						= max(a_						)

				lowerCAmelCase  :     list[float]       						= []
				# for weight 0 score is 1 - actual score
				if weight == 0:
						for item in dlist:
								try:
										score.append(1 - ((item - mind) / (maxd - mind))						)
								except ZeroDivisionError:
										score.append(1						)

				elif weight == 1:
						for item in dlist:
								try:
										score.append((item - mind) / (maxd - mind)						)
								except ZeroDivisionError:
										score.append(0						)

        # weight not 0 or 1
				else:
						lowerCAmelCase  :     List[Any]       						= f'''Invalid weight of {weight:f} provided'''
						raise ValueError(a_						)

				score_lists.append(a_						)

		return score_lists






def   __A    (				a_    :	list[list[float]]						):
		lowerCAmelCase  :     list[float]       						= [0 for i in range(len(score_lists[0]						)						)]

		for slist in score_lists:
				for j, ele in enumerate(a_						):
						lowerCAmelCase  :     Optional[Any]       						= final_scores[j] + ele

		return final_scores






def   __A    (				a_    :	list[list[float]]   ,a_    :	list[int]						):
		lowerCAmelCase  :     Union[str, Any]       						= get_data(a_						)
		lowerCAmelCase  :     List[Any]       						= calculate_each_score(a_   ,a_						)
		lowerCAmelCase  :     Optional[Any]       						= generate_final_scores(a_						)

		# append scores to source data
		for i, ele in enumerate(a_						):
				source_data[i].append(a_						)

		return source_data



